[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-123639-en":3,"doc-seo-123639-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},123639,549758146520,"Patrick","https://ap-avatar.wpscdn.com/avatar/80002397d8c0411e94?_k=1775819394049821470",8,"Research & Report","Rapid geographical source attribution of Salmonella enterica serovar Enteritidis genomes using hierarchical machine learning","Salmonella enterica serovar Enteritidis is a leading cause of salmonellosis worldwide and is often transmitted from animals to humans through contaminated food. In the UK and other Global North settings, many infections arise from imported foods or foreign travel, creating a need for fast, reliable geographical source identification in outbreak response. This work develops and applies a hierarchical machine learning model that attributes whole-genome sequencing isolates to continents, sub-regions, and countries, using 2,313 UKHSA genomes for training and validation across datasets.","RESEARCH ARTICLE  \n*For correspondence:  \n[s.bayliss@bristol.ac.uk](s.bayliss@bristol.ac.uk)  \nCompeting interest: The authors declare that no competing interests exist.  \nFunding: See page 17  \nPreprinted: 25 August 2022  \nReceived: 13 October 2022  \nAccepted: 02 April 2023  \nPublished: 12 April 2023  \nReviewing Editor: Ben SCooper, University of Oxford, United Kingdom  \n Copyright Bayliss et al. This article is distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use andredistribution provided that the original author and source are credited.  \nRapid geographical source attribution of Salmonella enterica serovar Enteritidis genomes using hierarchical machine learning  \nSion C Bayliss1*, Rebecca K Locke2,3, Claire Jenkins4, Marie Anne Chattaway4, Timothy J Dallman5, Lauren A Cowley2  \n1 Bristol Veterinary School, University of Bristol, Bristol, United Kingdom; 2 Milner Centre for Evolution, Life Sciences Department, University of Bath, Bath, United Kingdom; 3Genomic Laboratory Hub (GLH), Addenbrooke’s Hospital, Cambridge University Hospitals NHS Foundation Trust, Cambridge, United Kingdom; 4Gastrointestinal Reference Services, UK Health Security Agency, London, United Kingdom; 5 Institute for Risk Assessment Sciences, Utrecht University, Utrecht, Netherlands  \nAbstract Salmonella enterica serovar Enteritidis is one of the most frequent causes of Salmonellosis globally and is commonly transmitted from animals to humans by the consumption of contaminated foodstuffs. In the UK and many other countries in the Global North, a significant proportion of cases are caused by the consumption of imported food products or contracted during foreign travel, therefore, making the rapid identification of the geographical source of new infections a requirement for robust public health outbreak investigations. Herein, we detail the development and application of a hierarchical machine learning model to rapidly identify and trace the geographical source of S. Enteritidis infections from whole genome sequencing data. 2313 S. Enteritidis genomes, collected by the UKHSA between 2014–2019, were used to train a ‘local classifier per node’ hierarchical classifier to attribute isolates to four continents, 11 sub-regions, and 38 countries (53 classes) . The highest classification accuracy was achieved at the continental level followed by the sub-regional and country levels (macro F1: 0.954, 0.718, 0.661, respectively) . A number of countries commonly visited by UK travelers were predicted with high accuracy (hF1: >0.9) . Longitudinal analysis and validation with publicly accessible international samples indicated that predictions were robust to prospective external datasets. The hierarchical machine learning framework provided granular geographical source prediction directly from sequencing reads in \u003C4 min per sample, facilitating rapid outbreak resolution and real-time genomic epidemiology. The results suggest additional application to a broader range of pathogens and other geographically structured problems, such as antimicrobial resistance prediction, is warranted.  \nEditor's evaluation  \nThis important study presents a machine learning-based classifier that can accurately determine the geographic origin of a Salmonella enterica sample from its whole-genome sequencing data in under five minutes leading to actionable public health insights. Applying the method to 2,313 whole genome sequences collected in the United Kingdom and several external validation datasets, the authors provide convincing evidence that Salmonella genomic data can be used to identify the likely geographic source of a food-borne outbreak and, in most cases, correctly identify the country of  \nBayliss et al. eLife 2023;12:e84167. DOI: [https://doi.org/10.7554/eLife.84167](https://doi.org/10.7554/eLife.84167) 1 of 21  \n Research article Epidemiology and Global Health | Microbiology and Infectious Disease  \norigin of an infection acquir","cbCaijyjeXu9sZAe","https://ap.wps.com/l/cbCaijyjeXu9sZAe","pdf",6527535,1,21,"English","en",105,"# Abstract\n# Introduction\n## Foodborne transmission and outbreak challenges\n## Salmonella Enteritidis in the UK and risk drivers\n## Whole genome sequencing for transmission tracing","[{\"question\":\"What problem does the study address in Salmonella outbreak investigations?\",\"answer\":\"It addresses the need for rapid, robust identification of the geographical origin of new S. Enteritidis infections to support public health outbreak investigations.\"},{\"question\":\"How does the proposed method work?\",\"answer\":\"It uses a hierarchical machine learning classifier trained on whole genome sequencing data to attribute isolates to four continents, 11 sub-regions, and 38 countries.\"},{\"question\":\"How quickly can geographical predictions be produced and how was robustness tested?\",\"answer\":\"Predictions are generated directly from sequencing reads in under 4 minutes per sample, and longitudinal analysis with publicly accessible international samples indicates robustness on prospective external datasets.\"}]","Rapid geographical source attribution of Salmonella enterica serovar Enteritidis genomes using hierarchical machine learning | PDF",1785817774,53,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"rapid-geographical-source-attribution-of-salmonella-enterica-serovar-enteritidis-genomes-using-hierarchical-machine-learning","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/rapid-geographical-source-attribution-of-salmonella-enterica-serovar-enteritidis-genomes-using-hierarchical-machine-learning/123639/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-04",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What problem does the study address in Salmonella outbreak investigations?","Question",{"text":75,"@type":76},"It addresses the need for rapid, robust identification of the geographical origin of new S. Enteritidis infections to support public health outbreak investigations.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How does the proposed method work?",{"text":80,"@type":76},"It uses a hierarchical machine learning classifier trained on whole genome sequencing data to attribute isolates to four continents, 11 sub-regions, and 38 countries.",{"name":82,"@type":73,"acceptedAnswer":83},"How quickly can geographical predictions be produced and how was robustness tested?",{"text":84,"@type":76},"Predictions are generated directly from sequencing reads in under 4 minutes per sample, and longitudinal analysis with publicly accessible international samples indicates robustness on prospective external datasets.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,120,123,128,131,135],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":121,"slug":122},30,"research-report",{"id":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":132,"doc_module":4,"doc_module_name":46,"category_name":133,"show_sort_weight":132,"slug":134},10,"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":46,"category_name":137,"show_sort_weight":106,"slug":138},19,"General","general"]